Sosees04ka/PIbd-32_Kashin_M.I_API_CourseWork
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Короче, ничего не получилось что хотел :(

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Завтра сделаю фильтрованый датасет по городам, чтобы отдавать также инфу из городов
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maksim 2024-05-31 00:26:16 +04:00
parent b4dc220fe5
commit 1f5bd6bdbf
4 changed files with 8 additions and 6 deletions
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2
model.py
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@ -12,7 +12,7 @@ with open('neural_network/models/tokenization/tokenizer_lstm_lstm_negative.pickl
tokenizer = pickle.load(handle) tokenizer = pickle.load(handle)
# Загрузка названий классов # Загрузка названий классов
with open('neural_network/models/class/class_names_lstm_negative.txt', 'r', encoding='utf-8') as file: with open('neural_network/models/classification/class_names_lstm_negative.txt', 'r', encoding='utf-8') as file:
class_names = [line.strip() for line in file.readlines()] class_names = [line.strip() for line in file.readlines()]
def preprocess_text(text: str): def preprocess_text(text: str):

2
neural_network/class/class_save.py → neural_network/classification/class_save.py
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@ -38,7 +38,7 @@ def process_dataset(dataset_path, label_column, output_path):
dataset = label_processor.encode_labels() dataset = label_processor.encode_labels()
class_names = label_processor.get_class_names() class_names = label_processor.get_class_names()
# Save class names # Save classification names
label_processor.save_class_names(class_names, output_path) label_processor.save_class_names(class_names, output_path)
return dataset return dataset

6
neural_network/dataset/conversion.py
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@ -1,3 +1,5 @@
import sys
import kaggle import kaggle
import zipfile import zipfile
import os import os
@ -116,8 +118,8 @@ def main():
# Сохранение результатов # Сохранение результатов
FileSaver.save_rubrics_to_file(unique_rubrics_positive, 'class/class_positive.txt') FileSaver.save_rubrics_to_file(unique_rubrics_positive, 'class/class_positive.txt')
FileSaver.save_rubrics_to_file(unique_rubrics_negative, 'class/class_negative.txt') FileSaver.save_rubrics_to_file(unique_rubrics_negative, 'class/class_negative.txt')
FileSaver.save_dataset_to_csv(filtered_positive, '../dataset/filtered/filtered_dataset_positive.csv') FileSaver.save_dataset_to_csv(filtered_positive, 'filtered/filtered_dataset_positive.csv')
FileSaver.save_dataset_to_csv(filtered_negative, '../dataset/filtered/filtered_dataset_negative.csv') FileSaver.save_dataset_to_csv(filtered_negative, 'filtered/filtered_dataset_negative.csv')
if __name__ == "__main__": if __name__ == "__main__":

4
neural_network/models/create_model.py
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@ -206,7 +206,7 @@ class Plotter:
def main(): def main():
tokenizer_path_positive = '../tokenization/tokenizer_positive.pickle' tokenizer_path_positive = '../tokenization/tokenizer_positive.pickle'
class_names_path_positive = '../class/class_names_positive.txt' class_names_path_positive = '../classification/class_names_positive.txt'
dataset_path_positive = '../dataset/filtered/filtered_dataset_positive.csv' dataset_path_positive = '../dataset/filtered/filtered_dataset_positive.csv'
model_save_path_lstm_positive = './model/best_model_lstm_positive.keras' model_save_path_lstm_positive = './model/best_model_lstm_positive.keras'
@ -219,7 +219,7 @@ def main():
plot_save_path_gru_positive = './graphics/history_gru_positive.png' plot_save_path_gru_positive = './graphics/history_gru_positive.png'
tokenizer_path_negative = '../tokenization/tokenizer_negative.pickle' tokenizer_path_negative = '../tokenization/tokenizer_negative.pickle'
class_names_path_negative = '../class/class_names_negative.txt' class_names_path_negative = '../classification/class_names_negative.txt'
dataset_path_negative = '../dataset/filtered/filtered_dataset_negative.csv' dataset_path_negative = '../dataset/filtered/filtered_dataset_negative.csv'
model_save_path_lstm_negative = './model/best_model_lstm_negative.keras' model_save_path_lstm_negative = './model/best_model_lstm_negative.keras'